In the literature, few evaluation studies for comparing recent anomaly detection algorithms [1]}, [2]}, [3]} have been conducted.
For instance, Choi et al. [2]} present a brief comparison of recent DL algorithms in terms of precision, recall, and F1-score but neglect the model size and model stability. We can also mention the work of Lai et al. [3]}, where a new taxonomy for time-series outliers is proposed. Then, based on that, a methodology to generate synthetic datasets is suggested. They finally compare 9 different algorithms according to to the outlier types but do not include the latest DL algorithms. Moreover, similar to [2]}, they only focus on classical evaluation criteria. Finally, Kim et al. [1]} present a rigorous evaluation of recent DL techniques by questioning the point adjustment protocol. Nevertheless, the model size and model stability as well as the performance metrics for time-series are not considered.
